Sadayappan Kayalvizhi, Kerins Devon, Shen Chaopeng, Li Li
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.
Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.
Water Res. 2022 Nov 1;226:119295. doi: 10.1016/j.watres.2022.119295. Epub 2022 Oct 24.
Nitrate is one of the most widespread and persistent pollutants in our time. Our understanding of nitrate dynamics has advanced substantially in the past decades, although its predominant drivers across gradients of climate, land use, and geology have remained elusive. Here we collated nitrate data from 2061 rivers along with 32 watershed characteristic indexes and developed machine learning models to reconstruct long-term mean (multi-year average) nitrate concentrations in the contiguous United States (CONUS). The trained models show similarly satisfactory model performance and can predict nitrate concentrations in chemically-ungauged places with about 70% accuracy. Further analysis revealed that five (out of 32) indexes (drivers) can explain about 70% of spatial variations in mean nitrate concentrations. The five influential drivers are nitrogen application rates N and urban area A% (human drivers), mean annual precipitation and temperature (climate drivers), and sand percent Sand% (soil property driver). Nitrate concentrations in undeveloped sites are primarily modulated by climate and soil property; they decrease with increasing mean discharge and Sand%. Nitrate concentrations in agriculture and urban sites increase with N and A% until reaching their apparent maxima around 10,000 kg/km/yr and around 25%, respectively. Results indicate that nitrate concentrations may remain similar or increase with growing human population. In addition, nitrate concentrations can increase even without human input, as warming escalates water demand and reduces mean discharge in many places. These results allude to a conceptual model that highlights the impacts of distinct drivers: while human drivers predominate nitrogen input to land and rivers, climate drivers and soil properties modulate its transport and transformation, the balance of which determine long-term mean concentrations. Such mechanism-based insights and forecasting capabilities are essential for water management as we expect changing climate and growing agriculture and urbanization.
硝酸盐是当今最广泛且持久存在的污染物之一。在过去几十年里,我们对硝酸盐动态的理解有了显著进展,尽管其在气候、土地利用和地质梯度上的主要驱动因素仍不明确。在此,我们整理了来自2061条河流的硝酸盐数据以及32个流域特征指标,并开发了机器学习模型来重建美国本土(CONUS)的长期平均(多年平均)硝酸盐浓度。经过训练的模型显示出同样令人满意的模型性能,并且能够以约70%的准确率预测未进行化学测量地点的硝酸盐浓度。进一步分析表明,32个指标(驱动因素)中的5个能够解释平均硝酸盐浓度空间变化的约70%。这5个有影响力的驱动因素是氮肥施用量N和城市面积A%(人为驱动因素)、年平均降水量和温度(气候驱动因素)以及砂含量Sand%(土壤性质驱动因素)。未开发地区的硝酸盐浓度主要受气候和土壤性质调节;它们随着平均径流量和Sand%的增加而降低。农业和城市地区的硝酸盐浓度随着N和A%的增加而升高,直到分别达到约10000千克/平方千米/年和约25%时达到明显最大值。结果表明,随着人口增长,硝酸盐浓度可能保持不变或上升。此外,即使没有人类输入,随着气候变暖导致许多地方的用水需求增加和平均径流量减少,硝酸盐浓度也可能增加。这些结果暗示了一个概念模型,该模型突出了不同驱动因素的影响:虽然人为驱动因素主导了陆地和河流的氮输入,但气候驱动因素和土壤性质调节其迁移和转化,它们之间的平衡决定了长期平均浓度。随着我们预期气候不断变化以及农业和城市化不断发展,这种基于机制所获得的见解和预测能力对于水资源管理至关重要。